If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.
If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started!
The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

AA

A great introduction to understanding research and a great platform to springboard keen clinicians into performing their own research. Will take what I've learnt and apply it to my own research!

DS

May 27, 2018

Filled StarFilled StarFilled StarFilled StarFilled Star

I'm very new at this theme, this course has being the perfect beginning. If you don't have a mathematical background and you don't understand when the funny S appear, this is the course for you!

从本节课中

Describing your data

With the next topics, we finally get started with the statistics. Have you ever looked at the methods and results section of any healthcare research publication and noted the variety of statistical tests used? You would have come across terms like t-test, Mann-Whitney-U test, Wilcoxon test, Fisher’s exact test and the ubiquitous chi-squared test. Why so many tests you might wonder? It’s all about types of data. In this week, I am going to tackle the differences in data which determine what type of statistical test we can use in making sense of our data.

教学方

Juan H Klopper

Dr

脚本

Good, so now let's define some data points. Now just think of data, we can capture data. We can write it down on a piece of paper. We can be fancy and do a nice spreadsheet, columns of data, rows of data points or even fancier we can enter that data into a database. Now all of those data points they all belong to some variable, we're going to look at that. Now we're going do calculations, analysis of those data points and remember if it's a population it'll be a parameter, if it's a sample it'll be a statistic. But those data points or variables are all of a certain type. Now look at this, this table on screen here. You see the variables there, variable called pain, a variable called age, called gender, and called temperature and you see all the data values. Now, each of those columns of data values are of a different data type. Now I'm going to use two different definition systems or categories for these data types. The first one we're going to divide them into categorical and numerical data values and under those will be two again. Under the categorical, will have nominal and ordinal and under the numerical, we're going to have interval and the ratio. And then, we're going to move on the second classification system and I'm just going to define discrete and continuous data types.